Chainer is a deep learning framework which is flexible, intuitive, and powerful. This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsKusano Hitoshi
This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
Published on 11 may, 2018
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning), Chainer Chemistry (biology and chemistry), and ChainerUI (visualization).
Intro to TensorFlow and PyTorch Workshop at Tubular LabsKendall
These are some introductory slides for the Intro to TensorFlow and PyTorch workshop at Tubular Labs. The Github code is available at:
https://github.com/PythonWorkshop/Intro-to-TensorFlow-and-PyTorch
FCN-Based 6D Robotic Grasping for Arbitrary Placed ObjectsKusano Hitoshi
This is the slide used for IEEE International Conference on Robotics and Automation (ICRA) 2017, Workshop on Learning and Control for Autonomous Manipulation Systems on June 2nd, 2017.
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning)
Published on 11 may, 2018
Chainer is a deep learning framework which is flexible, intuitive, and powerful.
This slide introduces some unique features of Chainer and its additional packages such as ChainerMN (distributed learning), ChainerCV (computer vision), ChainerRL (reinforcement learning), Chainer Chemistry (biology and chemistry), and ChainerUI (visualization).
Intro to TensorFlow and PyTorch Workshop at Tubular LabsKendall
These are some introductory slides for the Intro to TensorFlow and PyTorch workshop at Tubular Labs. The Github code is available at:
https://github.com/PythonWorkshop/Intro-to-TensorFlow-and-PyTorch
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
This talk will provide an introduction to recurrent neural networks (RNNs). RNNs are designed to model sequential information and have provided impressive results for a variety of problems, such as speech recognition, language modeling, translation and image captioning. This talk walks through code in tensorflow for modeling a sine wave, performing basic addition, and generating handwriting. This was for a Chicago Tensorflow meetup in May 2016.
PyTorch crash course: Introduction to PyTorch deep learning framework and step by step guide to configuring PyCharm for using a remote server for implementing deep learning, plus a summary of Linux's most relevant commands.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
Let Android dream electric sheep: Making emotion model for chat-bot with Pyth...Jeongkyu Shin
summary
Chatbot is the underlying technology of an interactive interface. One of the problems to be solved for popularization of chatbots is the unnaturalness of inhuman conversation. This presentation introduces the process of implementing emotion status reading based on Python 3 for human conversation implementation, and the experience of simulating the emotional state of the bot itself, with the demonstration. We also share the problems and solutions we encountered in implementing the emotional models.
개요
챗봇은 대화형 인터페이스의 기반 기술이다. 챗봇의 대중화를 위해 해결해야 할 문제중 하나는 비인간적 대화에서 오는 부자연스러움이다. 이 발표에서는 인간적인 대화 구현을 위하여 Python 3를 기반으로 감정 상태 읽기를 구현한 과정과, 봇 자체의 감정 상태를 시뮬레이션한 경험을 데모와 함께 소개한다. 또한 감정 모형을 구현하는 과정에서 만났던 문제들 및 해결 방법을 공유한다.
상세
챗봇은 대화형 인터페이스 및 음성 인식과의 결합을 통한 무입력 방식 인터페이스의 기반 기술이다. 챗봇은 고객상담 서비스 분야부터 온라인 구매, 디지털 어시스턴트 등의 다양한 분야에 널러 사용되며, 텍스트 기반의 메신저부터 음성 인식 기반의 스마트 스피커등의 인터페이스를 통해 빠르게 보급되고 있다. 이러한 챗봇의 대중화는 최근 머신러닝을 기반으로 한 자연어 처리 기술의 성능 향상과, 딥러닝 분야의 발전에 힘입어 가능해진 end-to-end 모델 구현에 기술적으로 큰 영향을 받았다.
챗봇이 응용되는 분야가 넓어지고 다양한 분야에서 챗봇 서비스 및 사업이 성장함에 따라 '불편한 골짜기 ' 라고 불리는 비인간적 대화에서 오는 피로 문제가 점차 대두되고 있다. 비인간적 대화가 가져오는 피로는 사용자 경험 및 이용 지속성에 영향을 크게 미친다. 따라서 이 문제는 대화형 디지털 어시스턴트의 대중화 과정에서 어렵지만 우선적으로 해결해야 할 과제들 중 하나가 되었다.
감정 모형은 비인간적 대화를 벗어나 자연스러운 대화를 구현하기 위한 몇 가지 방법 중 하나이다. 이 발표에서는 Python 3를 기반으로 감정 상태 읽기를 구현하고, 감정상태를 시뮬레이션하는 과정에 대한 경험과 접근 방법을 소개한다. Python의 NLTK 패키지를 이용하여 감정 사전 데이터를 생성한다. 그 다음 기존 대화 데이터를 Python 3 및 Pandas를 이용하여 초벌가공한다. 가공한 데이터를 이용하여 wordvec 공간을 정의한다. Wordvec 공간의 각 단어에 감정 데이터를 이용해 만든 태그를 붙여 적절한 위상 공간을 정의한다. 이후 실시간 대화에서 들어오는 단어들을 일정 단위로 입력하여, 현재 화자의 감정 상태 및 감정 변화를 추적한다. 이후 봇의 감정을 담당하는 기계학습 모형을 만들어 학습시키고, 봇의 현재 감정에 따라 답변 문장을 변경하거나 기타 인터페이스를 통해 어필하도록 구현한다. 최종적으로는 봇 인터페이스 및 재미있는 인터페이스 아이디어와 함께 묶어 대화를 시연한다.
이 과정에서 겪은 문제들 및 해결 방법을 함께 소개한다. 우선 NLTK 패키지를 기반으로 감정 사전을 만드는 과정과, 감정 사전 인덱스를 한국어에 맞게 커스텀하는 과정을 설명한다. 그리고 감정 상태를 정의한 공간에서 봇의 감정 변화가 실제 인간과 다르게 심하게 튀는 문제를 고려하는 방법을 설명한다. 다양한 문제들의 해결 방법과 함께, 실제 서비스를 위해 멀티 모드 모델 체인에 컨텍스트 엔진 및 대화 엔진과 감정 모형을 연결하는 과정을 재미있는 데모와 함께 공유하고자 한다.
Fix typo of original tutorial slide.
Introduction of PyTorch
Explains PyTorch usages by a CNN example.
Describes the PyTorch modules (torch, torch.nn, torch.optim, etc) and the usages of multi-GPU processing.
Also gives examples for Recurrent Neural Network and Transfer Learning.
Deep Learning with TensorFlow: Understanding Tensors, Computations Graphs, Im...Altoros
1. The elements of Neural Networks: Weights, Biases, and Gating functions
2. MNIST (Hand writing recognition) using simple NN in TensorFlow (Introduce Tensors, Computation Graphs)
3. MNIST using Convolution NN in TensorFlow
4. Understanding words and sentences as Vectors
5. word2vec in TensorFlow
Introduction of Chainer, a framework for neural networks, v1.11. Slides used for the student seminar on July 20, 2016, at Sugiyama-Sato lab in the Univ. of Tokyo.
Distributed implementation of a lstm on spark and tensorflowEmanuel Di Nardo
Academic project based on developing a LSTM distributing it on Spark and using Tensorflow for numerical operations.
Source code: https://github.com/EmanuelOverflow/LSTM-TensorSpark
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
This talk will provide an introduction to recurrent neural networks (RNNs). RNNs are designed to model sequential information and have provided impressive results for a variety of problems, such as speech recognition, language modeling, translation and image captioning. This talk walks through code in tensorflow for modeling a sine wave, performing basic addition, and generating handwriting. This was for a Chicago Tensorflow meetup in May 2016.
PyTorch crash course: Introduction to PyTorch deep learning framework and step by step guide to configuring PyCharm for using a remote server for implementing deep learning, plus a summary of Linux's most relevant commands.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
Let Android dream electric sheep: Making emotion model for chat-bot with Pyth...Jeongkyu Shin
summary
Chatbot is the underlying technology of an interactive interface. One of the problems to be solved for popularization of chatbots is the unnaturalness of inhuman conversation. This presentation introduces the process of implementing emotion status reading based on Python 3 for human conversation implementation, and the experience of simulating the emotional state of the bot itself, with the demonstration. We also share the problems and solutions we encountered in implementing the emotional models.
개요
챗봇은 대화형 인터페이스의 기반 기술이다. 챗봇의 대중화를 위해 해결해야 할 문제중 하나는 비인간적 대화에서 오는 부자연스러움이다. 이 발표에서는 인간적인 대화 구현을 위하여 Python 3를 기반으로 감정 상태 읽기를 구현한 과정과, 봇 자체의 감정 상태를 시뮬레이션한 경험을 데모와 함께 소개한다. 또한 감정 모형을 구현하는 과정에서 만났던 문제들 및 해결 방법을 공유한다.
상세
챗봇은 대화형 인터페이스 및 음성 인식과의 결합을 통한 무입력 방식 인터페이스의 기반 기술이다. 챗봇은 고객상담 서비스 분야부터 온라인 구매, 디지털 어시스턴트 등의 다양한 분야에 널러 사용되며, 텍스트 기반의 메신저부터 음성 인식 기반의 스마트 스피커등의 인터페이스를 통해 빠르게 보급되고 있다. 이러한 챗봇의 대중화는 최근 머신러닝을 기반으로 한 자연어 처리 기술의 성능 향상과, 딥러닝 분야의 발전에 힘입어 가능해진 end-to-end 모델 구현에 기술적으로 큰 영향을 받았다.
챗봇이 응용되는 분야가 넓어지고 다양한 분야에서 챗봇 서비스 및 사업이 성장함에 따라 '불편한 골짜기 ' 라고 불리는 비인간적 대화에서 오는 피로 문제가 점차 대두되고 있다. 비인간적 대화가 가져오는 피로는 사용자 경험 및 이용 지속성에 영향을 크게 미친다. 따라서 이 문제는 대화형 디지털 어시스턴트의 대중화 과정에서 어렵지만 우선적으로 해결해야 할 과제들 중 하나가 되었다.
감정 모형은 비인간적 대화를 벗어나 자연스러운 대화를 구현하기 위한 몇 가지 방법 중 하나이다. 이 발표에서는 Python 3를 기반으로 감정 상태 읽기를 구현하고, 감정상태를 시뮬레이션하는 과정에 대한 경험과 접근 방법을 소개한다. Python의 NLTK 패키지를 이용하여 감정 사전 데이터를 생성한다. 그 다음 기존 대화 데이터를 Python 3 및 Pandas를 이용하여 초벌가공한다. 가공한 데이터를 이용하여 wordvec 공간을 정의한다. Wordvec 공간의 각 단어에 감정 데이터를 이용해 만든 태그를 붙여 적절한 위상 공간을 정의한다. 이후 실시간 대화에서 들어오는 단어들을 일정 단위로 입력하여, 현재 화자의 감정 상태 및 감정 변화를 추적한다. 이후 봇의 감정을 담당하는 기계학습 모형을 만들어 학습시키고, 봇의 현재 감정에 따라 답변 문장을 변경하거나 기타 인터페이스를 통해 어필하도록 구현한다. 최종적으로는 봇 인터페이스 및 재미있는 인터페이스 아이디어와 함께 묶어 대화를 시연한다.
이 과정에서 겪은 문제들 및 해결 방법을 함께 소개한다. 우선 NLTK 패키지를 기반으로 감정 사전을 만드는 과정과, 감정 사전 인덱스를 한국어에 맞게 커스텀하는 과정을 설명한다. 그리고 감정 상태를 정의한 공간에서 봇의 감정 변화가 실제 인간과 다르게 심하게 튀는 문제를 고려하는 방법을 설명한다. 다양한 문제들의 해결 방법과 함께, 실제 서비스를 위해 멀티 모드 모델 체인에 컨텍스트 엔진 및 대화 엔진과 감정 모형을 연결하는 과정을 재미있는 데모와 함께 공유하고자 한다.
Fix typo of original tutorial slide.
Introduction of PyTorch
Explains PyTorch usages by a CNN example.
Describes the PyTorch modules (torch, torch.nn, torch.optim, etc) and the usages of multi-GPU processing.
Also gives examples for Recurrent Neural Network and Transfer Learning.
Contents
Part I
Deep Learning for Medical Data Analysis Introduction
Automated Skin Cancer Classification
Automated Diabetic Retinopathy Classification
Brain Tumor Research
Alzheime Prediction
A Survey on Medical Image Deep Learning Research
Cardiac Arrhthymia Detection
ICU Patient Care
Part II
Deep Learning Introduction
Convolution Process Details
Issues with Big Data Deep Learning
Distributed Deep Learning for Medical Big Data Analysis
Challenges of Deep Learning for Medical Data Analysis
Content Based Image Retrieval (CBIR)
Part III
Xanadu Functionality
Xanadu Commodity Storage System Use Case
Xanadu Cloud Computing Use Case
Xanadu + Deep Learning + Hadoop + Spark Integration
Xanadu based Big Data Deep Learning System for Medical Data Analysis
Xanadu CBIR Demo
2017年春季研究発表会の発表資料です.
邦題: 形態素解析も辞書も言語モデルもいらないend-to-end音声認識
英題: End-to-end Japanese ASR without using morphological analyzer, pronunciation dictionary and language model
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Netflix success is credited to pioneering ways that the company introduced AI and ML into its products, services and infrastructure. ML learning is applied to solve a wide range of problems at Netflix.
Using Deep Learning Toolkits with Kubernetes clustersJoy Qiao
Slides for the talk at the O'Reilly AI Conference San Francisco 2017 - https://conferences.oreilly.com/artificial-intelligence/ai-ca/public/schedule/detail/59613
Startup.Ml: Using neon for NLP and Localization Applications Intel Nervana
Speaker: Arjun Bansal, co-founder of Nervana Systems
Arjun Bansal’s workshop focused on neon, an open-source python based deep learning framework that has been build from the ground up for speed and ease of use. The workshop highlights how to use neon, build Recurrent Recurrent Neural Networks to generate and analyze text, and build Convolutional Autoencoders to generate images and to localize objects. Arjun also demoed the integration of neon with the Nervana cloud (in private beta) for multi-GPU training of deep networks.
Urs Köster - Convolutional and Recurrent Neural NetworksIntel Nervana
Speaker: Urs Köster, PhD
Urs will join us to dive deep into the field of Deep Learning and focus on Convolutional and Recurrent Neural Networks. The talk will be followed by a workshop highlighting neon™, an open source python based deep learning framework that has been built from the ground up for speed and ease of use.
Netflix’s architecture involves thousands of microservices built to serve unique business needs. As this architecture grew, it became clear that the data storage and query needs were unique to each area; there is no one silver bullet which fits the data needs for all microservices. CDE (Cloud Database Engineering team) offers polyglot persistence, which promises to offer ideal matches between problem spaces and persistence solutions. In this meetup you will get a deep dive into the Self service platform, our solution to repairing Cassandra data reliably across different datacenters, Memcached Flash and cross region replication and Graph database evolution at Netflix.
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
Computer Vision abbreviated as CV aims to teach computers to achieve human level vision capabilities. Applications of CV in self driving cars, robotics, healthcare, education and the multitude of apps that allow customers to use the smartphone cameras to convey information has made it one of the most popular fields in Artificial Intelligence. The recent advances in Deep Learning, data storage and computing capabilities has lead to the huge success of CV. There are several tasks in computer vision, such as classification, object detection, image segmentation, optical character recognition, scene reconstruction and many others.
In this presentation I will talk about applying Transfer Learning, Image classification, object detection and the metrics required to measure them on still images. The increase in accuracy over of CV tasks over the past decade is due to Convolutional Neural Networks (CNN), CNN is the base used in architectures such as RESNET or VGGNET. I will go through how to use these pre-trained models for image classification and feature extraction. One of the break throughs in object detection has come with one-shot learning, where the bounding box and the class of the object is predicted simultaneously. This leads to low latency during inference (155 frames per second) and high accuracy. This is the framework behind object detection using YOLO , I will explain how to use yolo for specific use cases.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/amd/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-giduthuri
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Radhakrishna Giduthuri, Software Architect at Advanced Micro Devices (AMD), presents the "OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code" tutorial at the May 2018 Embedded Vision Summit.
OpenVX is an industry-standard computer vision and neural network inference API designed for efficient implementation on a variety of embedded platforms. The API incorporates the concept of a dataflow graph, which enables implementers to apply a range of optimizations appropriate to their architectures, such as image tiling and kernel fusion. Application developers can use this API to create high-performance computer vision and AI applications quickly, without having to perform complex device-specific optimizations for data management and kernel execution, since these optimizations are handled by the OpenVX implementation provided by the processor vendor.
This talk describes the current status of OpenVX, with particular focus on neural network inference capabilities and the most recent enhancements. The talk concludes with summary of the currently available implementations and an overview of the roadmap for the OpenVX API and its implementations.
NetflixOSS Meetup S3 E1, covering latest components in Distributed Databases, Telemetry systems, Big Data tools and more. Speakers from Netflix, IBM Watson, Pivotal and Nike Digital
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
2. Chainer – a deep learning framework
Chainer provides a set of features required for research and
development using deep learning such as designing neural
networks, training, and evaluation.
Designing a network Training, evaluation
Data
set
3. Features and Characteristics of Chainer
Powerful
☑ CUDA
☑ cuDNN
☑ NCCL
Versatile
☑ Convolutional Network
☑ Recurrent Network
☑ Many Other Components
☑ Various Optimizers
Intuitive
☑ Define-by-Run
☑ High debuggability
Supports GPU calculation using CUDA
High-speed training/inference by cuDNN
Supports a fast, multi-GPU learning using NCCL
N-dimensional Convolution, Deconvolution, Pooling, BN, etc.
RNN components such as LSTM, Bi-directional LSTM, GRU and Bi-directional GRU
Many layer definitions and various loss functions used in neural networks
Various optimizers, e.g., SGD, MomentumSGD, AdaGrad, RMSProp, Adam, etc.
Easy to write a complicated network
User-friendly error messages. Easy to debug using pure Python debuggers.
Well-abstracted common tools for various NN learning, easy to write a set of learning flows☑ Simple APIs
5. Neural network = Computational graph
NN can be interpreted as a computational graph that applies
many linear and nonlinear functions to input vectors
6. How to handle a computational graph
A definition of
computational graph
exists apart from code
that performs
computation according
to the definition
Static
The actual code that
performs computation is
treated as a definition of
computational graph
Dynamic
7. Chainer is the first deep-learning framework to adopt “Define-by-Run”*
How about Chainer? → Dynamic
● Define-and-Run(static graph)
Consists of two steps: first to build a computational graph, then feed data to the
computational graph (Caffe, theano, TensorFlow, etc.)
● Define-by-Run(dynamic graph)
Describing a forward-pass computation means to construct a computational
graph for the backward computation (Chainer, DyNet, PyTorch, etc.)
* autograd adopted Define-by-Run but it was not a framework for deep learning.
8. Define-and-Run and Define-by-Run
# Building
x = Variable(‘x’)
y = Variable(‘y’)
z = x + 2 * y
# Evaluation
for xi, yi in data:
eval(z, (xi, yi))
# Build, evaluate at the same time
for xi, yi in data:
x = Variable(xi)
y = Variable(yi)
z = x + 2 * y
You can make a branch to change
the forward computation
depending on the data
Define-and-Run Define-by-Run
9. How to write a Convolutional Network
import chainer
import chainer.links as L
import chainer.functions as F
class LeNet5(chainer.Chain):
def __init__(self):
super(LeNet5, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(1, 6, 5, 1)
self.conv2 = L.Convolution2D(6, 16, 5, 1)
self.conv3 = L.Convolution2D(16, 120, 4, 1)
self.fc4 = L.Linear(None, 84)
self.fc5 = L.Linear(84, 10)
• Start writing a model by inheriting Chain class
• Register parametric layers inside the init_scope
• Write forward computation in
__call__ method (no need to
write backward computation)
def __call__(self, x):
h = F.sigmoid(self.conv1(x))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv2(h))
h = F.max_pooling_2d(h, 2, 2)
h = F.sigmoid(self.conv3(h))
h = F.sigmoid(self.fc4(h))
return self.fc5(h)
10. Training models
model = LeNet5()
model = L.Classifier(model)
# Dataset is a list! ([] to access, having __len__)
dataset = [(x1, t1), (x2, t2), ...]
# iterator to return a mini-batch retrieved from dataset
it = iterators.SerialIterator(dataset, batchsize=32)
# Optimization methods (you can easily try various methods by changing SGD to
# MomentumSGD, Adam, RMSprop, AdaGrad, etc.)
opt = optimizers.SGD(lr=0.01)
opt.setup(model)
updater = training.StandardUpdater(it, opt, device=0) # device=-1 if you use CPU
trainer = training.Trainer(updater, stop_trigger=(100, 'epoch'))
trainer.run()
For more details, refer to official examples: https://github.com/pfnet/chainer/tree/master/examples
11. Define-by-Run brings flexibility and intuitiveness
“Forward computation” becomes a definition of network
• Depending on data, it is easy to change a network structure
• You can define a network itself by Python code
=The network structure can be treated as a program instead of data.
For Chainer, the “forward computation” can be written in Python
• Enables you to write a network structure freely using the syntax of Python
• Define-by-Run makes it easy to insert any process like putting a print statement between network
computations (In case of define-and-run which compiles a network, this kind of debugging is
difficult)
• Easy to reuse code of the same network for other purposes with few changes (e.g. by just adding
a conditional branch partially)
• Easy to check intermediate values and the design of the network itself using external debugging
tools etc.
12. Chainer v2.0.1
Significantly reduced memory consumption, organized API in response to the users feedback
Aggressive Buffer Release
to reduce the memory
consumption during
training→
CuPy has been released as an
independent library. This allows for
array operations using GPU via an
interface highly compatible with
NumPy.
https://cupy.chainer.org
https://chainer.org
13. CuPy
Independent library to handle all GPU calculations in Chainer
Lower cost to migrate CPU code to GPU with NumPy-compatible API
GPU-execute linear algebra algorithms such as a singular value decomposition
Rich in examples such as KMeans, Gaussian Mixture Model
import numpy as np
x = np.random.rand(10)
W = np.random.rand(10, 5)
y = np.dot(x, W)
import cupy as cp
x = cp.random.rand(10)
W = cp.random.rand(10, 5)
y = cp.dot(x, W)
GPU
https://github.com/cupy/cupy
14. Add-on packages for Chainer
Distribute deep learning, deep reinforcement learning, computer vision
ChainerMN (Multi-Node): additional package for distributed deep learning
High scalability (100 times faster with 128GPU)
ChainerRL: deep reinforcement learning library
DQN, DDPG, A3C, ACER, NSQ, PCL, etc. OpenAI Gym support
ChainerCV: provides image recognition algorithms, dataset wrappers
Faster R-CNN, Single Shot Multibox Detector (SSD), SegNet, etc.
16. ChainerMN: Multi-node
Keeping the easy-to-use characteristics of Chainer as is,
ChainerMN enables to use multiple nodes which have multiple
GPUs easily to make training faster
GPU
GPU
InfiniBand
GPU
GPU
InfiniBand
MPI
NVIDIA NCCL
18. Comparison with other frameworks
ChainerMN is the fastest at the comparison of elapsed time to train
ResNet-50 on ImageNet dataset for 100 epochs (May 2017)
19. We confirmed that if we increase the number of nodes,
the almost same accuracy can be achieved
Speedup without dropping the accuracy
21. Easy-to-use API of ChainerMN
You can start using ChainerMN just by wrapping one line!
optimizer = chainer.optimizers.MomentumSGD()
optimizer = chainermn.DistributedOptimizer(
chainer.optimizers.MomentumSGD())
22. ARM template will be announced soon
https://github.com/mitmul/ARMTeamplate4ChainerMN
↑ Click this to make a master node ↑ Click this to make worker nodes
23. Scaling via web interface
You can launch a scale-set of Azure instances super easily!
25. Reinforcement Learning:
ChainerRL: Deep Reinforcement Learning Library
Train an agent which interacts with the environment to maximize
the rewards
Action
Env
Observation, Reward
27. Distribution: Softmax, Mellowmax, Gaussian,…
Policy: Observation → Distribution of actions
2. Define an agent model
Reinforcement Learning with ChainerRL
28. 2. Define an agent model (contd.)
Q-Function: Observation → Value of each action (expectation of the sum of future rewards)
ActionValue: Discrete, Quadratic
Reinforcement Learning with ChainerRL
30. 4. Interact with the environment!
Reinforcement Learning with ChainerRL
31. Algorithms provided by ChainerRL
• Deep Q-Network (Mnih et al., 2015)
• Double DQN (Hasselt et al., 2016)
• Normalized Advantage Function (Gu et al., 2016)
• (Persistent) Advantage Learning (Bellemare et al., 2016)
• Deep Deterministic Policy Gradient (Lillicrap et al., 2016)
• SVG(0) (Heese et al., 2015)
• Asynchronous Advantage Actor-Critic (Mnih et al., 2016)
• Asynchronous N-step Q-learning (Mnih et al., 2016)
• Actor-Critic with Experience Replay (Wang et al., 2017) <- NEW!
• Path Consistency Learning (Nachum et al., 2017) <- NEW!
• etc.
32. ChainerRL Quickstart Guide
• Define a Q-function in a Jupyter notebook and learn the Cart Pole
Balancing problem with DQN
https://github.com/pfnet/chainerrl/blob/master/examples/quickstart/quickstart.ipynb
34. Evaluate your
model on
popular
datasets
Running and training deep-learning models easier for Computer Vision tasks
ChainerCV https://github.com/pfnet/chainercv
Datasets
Pascal VOC,
Caltech-UCSD
Birds-200-2011,
Stanford Online
Products, CamVid, etc.
Models
Faster R-CNN, SSD,
SegNet (will add more
models!)
Training
tools
Evaluation
tools
Dataset
Abstraction
Train popular
models with
your data
35. Start computer vision research using deep learning much easier
ChainerCV
Latest algorithms with your data
Provide complete model code, training code, inference code for segmentation
algorithms (SegNet, etc.) and object detection algorithms (Faster R-CNN, SSD,
etc.), and so on
All code is confirmed to reproduce the results
All training code and model code reproduced the experimental results shown in
the original paper
https://github.com/pfnet/chainercv
36. • If you want to see some
examples of ChainerCV
and the reproducing code
for some papers, please
check the official Github
repository
(chainer/chainercv)
• The right figure shows the
result of the inference code
of Faster RCNN example
• The pre-trained weights
are automatically
downloaded!
https://github.com/pfnet/chainercv
$ pip install chainercv
40. Intel Chainer with MKL-DNN Backend
CPU
CuPy
NVIDIA GPU
CUDA
cuDNN
BLAS
NumPy
Chainer
MKL-DNN
Intel Xeon/Xeon Phi
MKL
41. Intel Chainer with MKL-DNN Backend
MKL-DNN
• Neural Network library optimized for Intel architectures
• Supported CPUs:
✓ Intel Atom(R) processor with Intel(R) SSE4.1 support
✓ 4th, 5th, 6th and 7th generation Intel(R) Core processor
✓ Intel(R) Xeon(R) processor E5 v3 family (code named Haswell)
✓ Intel(R) Xeon(R) processor E5 v4 family (code named Broadwell)
✓ Intel(R) Xeon(R) Platinum processor family (code name Skylake)
✓ Intel(R) Xeon Phi(TM) product family x200 (code named Knights Landing)
✓ Future Intel(R) Xeon Phi(TM) processor (code named Knights Mill)
• MKL-DNN accelerates the computation of NN on the above CPUs
42. Intel Chainer with MKL-DNN Backend
convnet-benchmarks* result:
Intel Chainer Chainer with NumPy (MKL-Build)
Alexnet Forward 429.16 ms 5041.91 ms
Alexnet Backward 841.73 ms 5569.49 ms
Alexnet Total 1270.89 ms 10611.40 ms
~8.35x faster than NumPy backend!
43. Intel Chainer with MKL-DNN Backend
Intel is developing Intel Chainer as a fork of Chainer v2
https://github.com/intel/chainer
47. Ponanza Chainer
● Won the 2nd
place at The 27th
World Computer Shogi Championship
● Based on Ponanza which was the champion for two years in a row (2015, 2016)
● “Ponanza Chainer” applied Deep Learning for ordering the possible next moves for which
“Ponanza” should think ahead deeply
● “Ponanza Chainer” wins “Ponanza” with a probability of 80%
Team
PFN
Issei
Yamamoto
Akira
Shimoyama
Team
Ponanza
48. Paints Chainer
● Auto Sketch Colorization
● Train a neural network with
a large dataset of paintings
● It takes a line drawings as
input, and output a
colorized image!
● You can also give color hits
which indicates preferable
colors
https://paintschainer.preferred.tech
50. 1. Install CUDA Toolkit 8.0
https://developer.nvidia.com/cuda-downloads
2. Install cuDNN v6.0 Library
https://developer.nvidia.com/rdp/cudnn-download
3. Install NCCL for Multi-GPUs
https://github.com/NVIDIA/nccl
4. Install CuPy and Chainer
% pip install cupy
% pip install chainer
Chainer on Ubuntu
For more details, see the official installation guide:
http://docs.chainer.org/en/stable/install.html
51. Chainer on Windows with NVIDIA GPU
1. Install Visual C++ 2015 Build Tools
http://landinghub.visualstudio.com/visual-cpp-build-tools
2. Install CUDA Toolkit 8.0
https://developer.nvidia.com/cuda-downloads
3. Install cuDNN v6.0 Library for Windows 10
https://developer.nvidia.com/rdp/cudnn-download
Put all files under C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0
4. Install Anaconda 4.3.1 Python 3.6 or 2.7
https://www.continuum.io/downloads
5. Add environmental variables
- Add “C:Program Files (x86)Microsoft Visual Studio 14.0VCbin” to PATH variable
- Add “C:Program Files (x86)Windows Kits10Include10.0.10240.0ucrt” to INCLUDE variable
6. Install Chainer on Anaconda Prompt
> pip install chainer
52. Chainer on Azure
Use Data Science Virtual Machine for Linux (Ubuntu)
•Ready for CUDA 8.0 & cuDNN 5.1
•After ssh, ”pip install --user chainer”
1
2
3
53. Chainer Model Export
tfchain: TensorFlow export (experimental)
Caffe-export: Caffe export (experimental)
• https://github.com/mitmul/tfchain
• Supports Linear, Convolution2D, MaxPooling2D, ReLU
• Just add @totf decorator right before the forward method of the model
• Currently closed project
• Supports Conv2D, Deconv2D, BatchNorm, ReLU, Concat, Softmax,
Reshape
54. External Projects for Model Portability
DLPack
• https://mil-tokyo.github.io/webdnn/
• The model conversion to run it on a web browser supports Chainer
WebDNN
• https://github.com/dmlc/dlpa
ck
• MXNet, Torch, Caffe2 have
joined to discuss the
guideline of memory layout
of tensor and the common
operator interfaces
58. Chainer is an open-source project.
• You can send a PR from here: https://github.com/chainer/chainer
• The development speed of Deep Learning research is super fast, therefore,
to provide the state-of-the-art technologies through Chainer, we
continuously update the development plans:
• Chainer v3.0.0 will be released on 26th
September!
• Will support gradient of gradient (higher order differentiation)
• Will add the official Windows support ensured by Microsoft
The release schedule after
v2.0.1 (4th
July)→